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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.19359 |
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| _version_ | 1866909658810155008 |
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| author | Bhaumik, Debosmita Togelius, Julian Yannakakis, Georgios N. Khalifa, Ahmed |
| author_facet | Bhaumik, Debosmita Togelius, Julian Yannakakis, Georgios N. Khalifa, Ahmed |
| contents | We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other performance characteristics. The repair problem may also be constrained in that it can only make a small number of changes to the level. We investigate search-based solutions to the level repair problem, particularly using evolutionary and quality-diversity algorithms, with good results. This level repair method is applied to levels generated using a machine learning-based procedural content generation (PCGML) method that generates stylistically appropriate but frequently broken levels. This combination of PCGML for generation and search-based methods for repair shows great promise as a hybrid procedural content generation (PCG) method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evolutionary Level Repair Bhaumik, Debosmita Togelius, Julian Yannakakis, Georgios N. Khalifa, Ahmed Artificial Intelligence We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other performance characteristics. The repair problem may also be constrained in that it can only make a small number of changes to the level. We investigate search-based solutions to the level repair problem, particularly using evolutionary and quality-diversity algorithms, with good results. This level repair method is applied to levels generated using a machine learning-based procedural content generation (PCGML) method that generates stylistically appropriate but frequently broken levels. This combination of PCGML for generation and search-based methods for repair shows great promise as a hybrid procedural content generation (PCG) method. |
| title | Evolutionary Level Repair |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19359 |